Tracking Mouse from Incomplete Body-Part Observations and Deep-Learned Deformable-Mouse Model Motion-Track Constraint for Behavior Analysis
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arXiv
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| Autori principali: | , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866916572167143424 |
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| author | Hellwich, Olaf Andresen, Niek Hohlbaum, Katharina Boon, Marcus N. Kwiatkowski, Monika Matern, Simon Reiske, Patrik Sprekeler, Henning ThöneReineke, Christa Lewejohann, Lars Zada, Huma Ghani Brück, Michael Traverso, Soledad |
| author_facet | Hellwich, Olaf Andresen, Niek Hohlbaum, Katharina Boon, Marcus N. Kwiatkowski, Monika Matern, Simon Reiske, Patrik Sprekeler, Henning ThöneReineke, Christa Lewejohann, Lars Zada, Huma Ghani Brück, Michael Traverso, Soledad |
| contents | Tracking mouse body parts in video is often incomplete due to occlusions such that - e.g. - subsequent action and behavior analysis is impeded. In this conceptual work, videos from several perspectives are integrated via global exterior camera orientation; body part positions are estimated by 3D triangulation and bundle adjustment. Consistency of overall 3D track reconstruction is achieved by introduction of a 3D mouse model, deep-learned body part movements, and global motion-track smoothness constraint. The resulting 3D body and body part track estimates are substantially more complete than the original single-frame-based body part detection, therefore, allowing improved animal behavior analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_11030 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Tracking Mouse from Incomplete Body-Part Observations and Deep-Learned Deformable-Mouse Model Motion-Track Constraint for Behavior Analysis Hellwich, Olaf Andresen, Niek Hohlbaum, Katharina Boon, Marcus N. Kwiatkowski, Monika Matern, Simon Reiske, Patrik Sprekeler, Henning ThöneReineke, Christa Lewejohann, Lars Zada, Huma Ghani Brück, Michael Traverso, Soledad Computer Vision and Pattern Recognition Tracking mouse body parts in video is often incomplete due to occlusions such that - e.g. - subsequent action and behavior analysis is impeded. In this conceptual work, videos from several perspectives are integrated via global exterior camera orientation; body part positions are estimated by 3D triangulation and bundle adjustment. Consistency of overall 3D track reconstruction is achieved by introduction of a 3D mouse model, deep-learned body part movements, and global motion-track smoothness constraint. The resulting 3D body and body part track estimates are substantially more complete than the original single-frame-based body part detection, therefore, allowing improved animal behavior analysis. |
| title | Tracking Mouse from Incomplete Body-Part Observations and Deep-Learned Deformable-Mouse Model Motion-Track Constraint for Behavior Analysis |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2501.11030 |